$ ~/registry/skill/anthropics-bio-research-skills-scvi-tools

SKILL

scvi-tools

Name: scvi-tools
Author: anthropics

Run deep-learning single-cell analyses for integration, multimodal modeling, and mapping.

Stars

★ 22,528

Source

GitHub

Updated

2026-07-11

// securityLow risk

Prompt only, no code
Open source
Community-trusted· 22.5k

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// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "scvi-tools" skill from askskill:
1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/bio-research/skills/scvi-tools/SKILL.md
2. Save it as ~/.claude/skills/scvi-tools/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// examples

Batch correction and data integration

Prompt

I have two single-cell RNA-seq datasets from different batches. Use scVI or scANVI for data integration and batch correction, and explain how to build the latent space, assess integration quality, and produce visualization outputs.

Expected

A scVI/scANVI workflow with key parameter guidance, integration evaluation methods, and visualization deliverables.

CITE-seq multimodal analysis

Prompt

Please use totalVI to analyze my CITE-seq data, jointly model RNA and protein expression, perform denoising and clustering, interpret cell types, and explain which features are most discriminative.

Expected

Returns a totalVI analysis workflow, joint embeddings, clustering and cell-type interpretation, plus a summary of key RNA/protein features.

Reference mapping and label transfer

Prompt

I want to map new single-cell samples onto an existing reference atlas. Use scANVI or scArches to design a label transfer and reference mapping workflow, and explain how to handle unknown cell types and batch differences.

Expected

Provides a reference mapping plan, label transfer steps, recommendations for unknown classes, and methods to control batch effects.

// docs

scvi-tools Deep Learning Skill

This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.

How to Use This Skill

Identify the appropriate workflow from the model/workflow tables below
Read the corresponding reference file for detailed steps and code
Use scripts in scripts/ to avoid rewriting common code
For installation or GPU issues, consult references/environment_setup.md
For debugging, consult references/troubleshooting.md

When to Use This Skill

When scvi-tools, scVI, scANVI, or related models are mentioned
When deep learning-based batch correction or integration is needed
When working with multi-modal data (CITE-seq, multiome)
When reference mapping or label transfer is required
When analyzing ATAC-seq or spatial transcriptomics data
When learning latent representations of single-cell data

Model Selection Guide

Data Type	Model	Primary Use Case
scRNA-seq	scVI	Unsupervised integration, DE, imputation
scRNA-seq + labels	scANVI	Label transfer, semi-supervised integration
CITE-seq (RNA+protein)	totalVI	Multi-modal integration, protein denoising
scATAC-seq	PeakVI	Chromatin accessibility analysis
Multiome (RNA+ATAC)	MultiVI	Joint modality analysis
Spatial + scRNA reference	DestVI	Cell type deconvolution
RNA velocity	veloVI	Transcriptional dynamics
Cross-technology	sysVI	System-level batch correction

Workflow Reference Files

Workflow	Reference File	Description
Environment Setup	`references/environment_setup.md`	Installation, GPU, version info
Data Preparation	`references/data_preparation.md`	Formatting data for any model
scRNA Integration	`references/scrna_integration.md`	scVI/scANVI batch correction
ATAC-seq Analysis	`references/atac_peakvi.md`	PeakVI for accessibility
CITE-seq Analysis	`references/citeseq_totalvi.md`	totalVI for protein+RNA
Multiome Analysis	`references/multiome_multivi.md`	MultiVI for RNA+ATAC
Spatial Deconvolution	`references/spatial_deconvolution.md`	DestVI spatial analysis
Label Transfer	`references/label_transfer.md`	scANVI reference mapping
scArches Mapping	`references/scarches_mapping.md`	Query-to-reference mapping
Batch Correction	`references/batch_correction_sysvi.md`	Advanced batch methods
RNA Velocity	`references/rna_velocity_velovi.md`	veloVI dynamics
Troubleshooting	`references/troubleshooting.md`	Common issues and solutions

CLI Scripts

Modular scripts for common workflows. Chain together or modify as needed.

Pipeline Scripts

Script	Purpose	Usage
`prepare_data.py`	QC, filter, HVG selection	`python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch`
`train_model.py`	Train any scvi-tools model	`python scripts/train_model.py prepared.h5ad results/ --model scvi`
`cluster_embed.py`	Neighbors, UMAP, Leiden	`python scripts/cluster_embed.py adata.h5ad results/`
`differential_expression.py`	DE analysis	`python scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden`
`transfer_labels.py`	Label transfer with scANVI	`python scripts/transfer_labels.py ref_model/ query.h5ad results/`
`integrate_datasets.py`	Multi-dataset integration	`python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad`
`validate_adata.py`	Check data compatibility	`python scripts/validate_adata.py data.h5ad --batch-key batch`

Example Workflow

# 1. Validate input data
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest

# 2. Prepare data (QC, HVG selection)

…

View full docs ↗

anthropicsGet→

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